Biology subjects

Joyner, K.

Publications and source records attributed to Joyner, K..

3 recordsLinked to original sources

bioRxiv2026

Evaluating the reliability of functional near-infrared spectroscopy data in the context of a reasoning paradigm

Functional near-infrared spectroscopy (fNIRS) is a portable, motion-tolerant neuroimaging method particularly well suited for developmental and naturalistic research. To evaluate the utility of fNIRS for studying individual differences and longitudinal changes, we measured activation and functional connectivity during a relational reasoning task in young adults (N = 73). We sought to (1) establish whether fNIRS captures frontoparietal activation patterns consistent with prior fMRI studies using similar paradigms, (2) assess the effect of the amount of data (number of task blocks) on signal strength and precision, (3) assess the paradigms measurement properties in the form of intra- and interindividual stability of activation and functional connectivity within and across testing sessions, and (4) examine whether grouping channels into anatomical regions of interest (ROIs) conferred benefits to the above. We observed robust task-evoked activation across lateral prefrontal and parietal cortices, with effect sizes on par with prior fMRI studies. Generally, we observed diminishing returns in effect size and measurement precision beyond [~]7 minutes. Internal consistency and test-retest reliability varied across metrics; while they were very low for a specific task contrast, they were extremely high for functional connectivity, confirming the robustness of channel- and ROI-level connectivity as a stable marker of functional architecture. Exploratory analyses supported prior observations of lower signal quality in participants with darker skin tones and hair, underscoring the need for inclusive methodological strategies. Together, these findings highlight key design considerations for optimizing longitudinal and individual-differences research on higher-level cognition, particularly in diverse and developmentally variable populations. HighlightsO_LIWe measured within- and between-session reliability of fNIRS metrics C_LIO_LICollecting more data yielded diminishing returns in effect size and precision C_LIO_LIThere were tradeoffs to aggregating channel data into regions of interest C_LIO_LIGeneral task activation was more reliable than a specific task contrast C_LIO_LIFunctional connectivity showed extremely high test-retest reliability C_LI

neuroscience↗

bioRxiv2022

Machine Learning Informed Predictions of Nanoparticle Mobility and Fate in the Mucus Barrier

Diffusion and transport of nanomaterials through mucus, is of critical importance to many basic and applied areas of research such as drug delivery and infectious disease. However, it is often challenging to interpret the dynamics of nanoparticles within the mucus gel due to its inherently heterogeneous microstructure and biochemistry. In this study, we measured the diffusion of densely PEGylated nanoparticles (NP) in human airway mucus ex vivo using multiple particle tracking and utilized machine learning to classify NP movement as either traditional Brownian motion (BM) or one of two different models of anomalous diffusion, fractional Brownian motion (FBM) and continuous time random walk (CTRW). Specifically, we employed a physics-based neural network model to predict the modes of diffusion experienced by individual NP in human airway mucus. We observed rapidly diffusing NP primarily exhibit BM whereas CTRW and FBM exhibited lower diffusion rates. Given the use of muco-inert nanoparticles, the observed transition from diffusive (BM) to sub-diffusive (CTRW/FBM) motion is likely a result of patient-to-patient variation in mucus network pore size. Using mathematic models that account for the mode of NP diffusion, we predicted the percentage of nanoparticles that would cross the mucus barrier over time in human airway mucus with varied total solids concentration. We also applied this approach to explore the transport modes and predicted fate of influenza A virus within human mucus. These results provide new tools to evaluate the extent of synthetic and viral nanoparticle penetration through mucus in the lung and other tissues.

bioengineering↗

bioRxiv2019

Designing viscoelastic mucin-based hydrogels

We report the design of a mucin hydrogel created using a thiol-based cross-linking strategy. By using a cross-linking reagent capable of forming disulfide linkages between mucins, the mucin-based hydrogels possess viscoelastic properties comparable to native mucus as measured by bulk rheology. We confirmed disulfide cross-links mediate gel formation in our system using chemical treatments to block and reduce cysteines where we found mucin hydrogel network formation was inhibited and disrupted, respectively. Particle tracking microrheology was used to investigate the kinetics and evolution of microstructure and viscoelasticity within the hydrogel as it formed. We found that the rate of gel formation could be tuned by varying the mucin to crosslinker ratio, producing network pore sizes in the range measured previously in human mucus. The results of this work provide a new, simple method for creating mucin hydrogels with physiologically relevant properties using readily available reagents.

bioengineering↗